Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization.

Hannah Dröge, Baichuan Yuan, Rafael Llerena, Jesse T Yen, Michael Moeller, Andrea L Bertozzi

Abstract read

In one paragraph

Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

6 authors.

Hannah DrögeDepartment of Electrical Engineering and Computer Science, University of Siegen, 57076 Siegen, Germany.ORCID 0000-0001-7163-4279

Baichuan YuanDepartment of Mathematics, University of California, Los Angeles, CA 90095, USA.

Rafael LlerenaNon-Invasive Cardiology Department, Keck Medical Center of University of Southern California, Los Angeles, CA 90033, USA.

Jesse T YenDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.

Michael MoellerDepartment of Electrical Engineering and Computer Science, University of Siegen, 57076 Siegen, Germany.

Andrea L BertozziDepartment of Mathematics, University of California, Los Angeles, CA 90095, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analyzing and understanding the movement of the mitral valve is of vital importance in cardiology, as the treatment and prevention of several serious heart diseases depend on it. Unfortunately, large amounts of noise as well as a highly varying image quality make the automatic tracking and segmentation of the mitral valve in two-dimensional echocardiographic videos challenging. In this paper, we present a fully automatic and unsupervised method for segmentation of the mitral valve in two-dimensional echocardiographic videos, independently of the echocardiographic view. We propose a bias-free variant of the robust non-negative matrix factorization (RNMF) along with a window-based localization approach, that is able to identify the mitral valve in several challenging situations. We improve the average f1-score on our dataset of 10 echocardiographic videos by 0.18 to a f1-score of 0.56.

Indexed as

echocardiographymitral valverobust non-negative matrix factorizationsegmentation

Identifiers

PMID34677299

DOI10.3390/jimaging7100213

PMCPMC8541511

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.